Consumer packaged goods (CPGs) have always been particularly dynamic and competitive. But demanding consumer expectations now mean that organisations must constantly adapt.
Hyper-personalisation has evolved from a competitive advantage to an essential component of effective customer engagement strategies. It allows you to create deeper connections with your consumers through tailored experiences. Leaders in the market are already seeing the benefits—for example, Coca-Cola’s use of integrated generative AI to deliver personalised marketing saw a 25% boost in online sales.¹ Sephora’s various personalisation features, including the use of AR tech to create an interactive virtual “try-on” experience, delivered a 4x increase in online sales over 6 years.²
But hyper-personalisation relies on exceptional data capabilities. So, to truly understand your customer, we’ve identified three key investment areas that CPGs can use to achieve this:
1. Understand your primary and market data
The data sources to deliver your target experience are key to building a unified customer profile. This profile should integrate data from all touchpoints, including a combination of first-party data (e.g., social media engagement, if managed in-house) and third-party data (e.g., purchase data via retail platforms). CPGS must clearly define and focus on the most critical data touchpoints.
2. Define your target data architecture
Next up is deciding whether to buy an off-the-shelf solution or invest in building your own platform. This depends on your organisation’s architecture principles, timelines, capabilities and strategic goals. CPGs face challenges in data architecture with incredibly diverse data sources, both CPG-owned and third-party (retailer) owned, that they must bring together. Establishing required data models, data lineage and connections is therefore vital for CPGs starting to invest in data architecture. You should also leverage data lake and warehouse integration to centralise data sources into a single data format. This creates synergies between original and operational data sets and a potential analytical system, supporting you in harnessing AI-powered analytics to identify the appropriate marketing, product, or pricing strategy based on consumer insight.
For example, Nestlé transformed its data landscape to revolutionise customer engagement and accelerate innovation. The company created a unified data ecosystem through successful cloud migration, enabling seamless global collaboration and scalable data management. Additionally, by harnessing AI to analyse consolidated customer insights and market trends, Nestlé developed highly personalised marketing strategies and tailored product offerings. This resulted in significant revenue growth and enhanced customer satisfaction.
3. Form robust data governance and management
Finally, data governance should lay the foundations for any data transformation with clear owners. Effective data governance allows CPGs to break down organisational silos, ensuring data quality and consistency across multiple channels and touchpoints. A single source of truth will support advanced analytics and predictive modelling. In highly regulated environments, a comprehensive data governance strategy mitigates compliance risks while building consumer trust.
YOUR OPPORTUNITY TO LEVERAGE DATA
Investing in data and establishing a single source of primary and market data with the required architecture and governance, CPGs can unlock a huge opportunity to harness the power of data and AI. By treating data as a strategic asset, companies can develop a comprehensive approach to understanding consumer behaviour, synthesising insights from diverse sources to uncover not just what consumers do, but the deeper motivations behind their choices. This holistic perspective enables truly personalised experiences that can meaningfully engage customers, ultimately driving loyalty and sustainable revenue growth in an increasingly competitive market.
We take a human-centric approach to data transformation to create a data-driven enterprise. If you would like support with your data strategy, we’d love to chat.
